{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import libpysal as lps\n",
    "import numpy as np\n",
    "import esda\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = lps.weights.lat2W(4, 4)\n",
    "y = np.ones(16)\n",
    "y[0:8] = 0\n",
    "np.random.seed(12345)\n",
    "from esda.join_counts import Join_Counts\n",
    "jc = Join_Counts(y, w)\n",
    "jc.bb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Neighbor</th>\n",
       "      <th>W</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Focal</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Neighbor     W     B\n",
       "Focal               \n",
       "W         10.0   1.0\n",
       "B          3.0  10.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jc.crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Neighbor</th>\n",
       "      <th>W</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Focal</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>5.958333</td>\n",
       "      <td>5.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>7.041667</td>\n",
       "      <td>5.958333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Neighbor         W         B\n",
       "Focal                       \n",
       "W         5.958333  5.041667\n",
       "B         7.041667  5.958333"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jc.expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.479632255856034"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jc.chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.003591446953916693"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jc.chi2_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jc.p_sim_chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f3fecbd5be0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, figsize=(9, 9))\n",
    "sns.distplot(jc.sim_chi2, rug=True, hist=False, ax=ax)\n",
    "plt.axvline(jc.chi2, 0,0.17, color='r')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'BB Counts')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, figsize=(9, 9))\n",
    "sns.distplot(jc.sim_bb, rug=True, hist=False, ax=ax)\n",
    "plt.axvline(jc.bb, 0,0.17, color='r')\n",
    "plt.title('BB Counts')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'WW Counts')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, figsize=(9, 9))\n",
    "sns.distplot(jc.sim_bw, rug=True, hist=False, ax=ax)\n",
    "plt.axvline(jc.bw, 0,0.17, color='r')\n",
    "plt.title('WW Counts')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
